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To facilitate the emerging applications in 5G networks, mobile networkoperators will provide many network functions in terms of control andprediction. Recently, they have recognized the power of machine learning (ML)and started to explore its potential to facilitate those network functions.Nevertheless, the current ML models for network functions are often derived inan offline manner, which is inefficient due to the excessive overhead fortransmitting a huge volume of dataset to remote ML training clouds and failingto provide the incremental learning capability for the continuous modelupdating. As an alternative solution, we propose Cocktail, an incrementallearning framework within a reference 5G network architecture. To achieve costefficiency while increasing trained model accuracy, an efficient online datascheduling policy is essential. To this end, we formulate an online datascheduling problem to optimize the framework cost while alleviating the dataskew issue caused by the capacity heterogeneity of training workers from thelong-term perspective. We exploit the stochastic gradient descent to devise anonline asymptotically optimal algorithm, including two optimal policies basedon novel graph constructions for skew-aware data collection and data training.Small-scale testbed and large-scale simulations validate the superiorperformance of our proposed framework.

Cost-efficient and Skew-aware Data Scheduling for Incremental Learning in 5G Network